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BP神经网络在测井岩性识别中的应用 总被引：27，自引：0，他引：27
在岩性识别方法中，人工神经网络方法由于其识别结果客观可靠，得到越来越广泛的应用。研究选用BP神经网络，对金衢盆地的金66测井的岩性进行了识别，并对改善BP神经网络收敛性能的方法了有效探索。 相似文献
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BP神经网络识别塔北低阻油气层 总被引：10，自引：1，他引：10
简要介绍了塔北低阻油气层岩性剖面、低阻油气层地球物理测井曲线特征,分析了塔北地区低阻油气储层成因,重点论述BP人工神经网络识别油气层、油水同层、水层和干层的方法原理。识别实例表明,BP人工神经网络识别低阻油(气)、水层的结果与实际相符,明显地提高了测井的解释精度。 相似文献
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人工神经网络具有很强的非线性处理能力,能够有效地模拟复杂的非线性径流预报过程。传统的基于BP训练算法的人工神经网络具有训练时间较长,容易陷于局部最优值等缺陷,本文对训练算法加以改进,分别使用平均线性粒子群,粒子群和BP算法来优化人工神经网络的各项参数,首先使用标准函数测试了3种算法的全局优化性能,然后用它们对三峡水库的入库径流进行预报,以比较它们的预报性能。结果表明,在3种算法中,平均线性粒子群算法全局寻优的速度最快,稳定性最高,基于平均线性粒子群算法的人工神经网络的径流预报的精度也最高。 相似文献
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建筑物震陷预测新方法研究 总被引：1，自引：0，他引：1
利用人工神经网络的基本原理，本文修正了经典BP型神经网络的激励函数，并对学习率和训练样本进行了动态调整等多方面改进。根据70个多层建筑震陷的实测资料，在分析了建筑物震陷的影响因素基础上，提取了9个指标；采用改进后的BP算法，建立了多指标的建筑物震陷预测模型。研究结果表明，改进的BP网络性能良好，所建立的模型预测精度高，具有一定的工程实用价值；神经网络法是一种有效可行的预测新方法，人工神经网络技术具有广泛的应用前景。 相似文献
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RPROP算法在测井岩性识别中的应用 总被引：4，自引：1，他引：3
为了更好地解决测井岩性识别问题,引入一种快速实用的BP算法--Resilient Backpropagation (RPROP)算法。在说明RPROP算法的基础上,结合某地的实际测井资料,建立基于RPROP算法的BP网络岩性识别模型,进行岩性识别的应用研究。结果表明,应用RPROP算法进行测井资料岩性识别,识别的准确率较高,与基本BP算法及其一些改进算法相比,训练速度快,具有很好的应用前景。 相似文献
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对用人工神经网络方法来解决钻探生产的实际问题, 在不取心的情况下识别所钻地层的岩性进行了研究.根据钻探生产的特点, 设计了人工神经网络的结构和输出方式, 开发了人工神经网络识别所钻地层的软件, 分析了影响人工神经网络应用效果的各因素, 在人工神经网络的优化设计方面作了较深入的研究.研究表明: 人工神经网络用于识别所钻地层有很好的效果; 人工神经网络的参数, 如学习率、隐含层层数、隐含层单元数和数据处理方式等对人工神经网络的应用效果有影响. 相似文献
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三种基于神经网络的洪水实时预报方案的比较研究 总被引：8，自引：1，他引：7
在总结神经网络应用的基础上,归纳了3种基于神经网络的洪水实时预报方案。第一种是神经网络水文模型的模拟模式加模拟误差的自回归校正模型,第二种是权重系数固定的神经网络实时预报方案,第三种是权重系数自动更新的神经网络实时预报方案。采用10个不同流域的日流量资料对这3种方案进行率定和校核。比较这3种方案的实时预报精度。结果发现,第三种方案不仅预报精度要高于其他两种方案,而且比第一种方案少了一个自回归校正模型,结构简洁。本文建议采用第三种洪水实时预报方案。 相似文献
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Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks 总被引：2，自引：1，他引：1
An artificial neural network (ANN) model is proposed for the simultaneous determination of transmissivity and storativity
distributions of a heterogeneous aquifer system. ANNs may be useful tools for parameter identification problems due to their
ability to solve complex nonlinear problems. As an extension of previous study—Karahan H, Ayvaz MT (2006) Forecasting aquifer
parameters using artificial neural networks, J Porous Media 9(5):429–444—the performance of the proposed ANN model is tested
on a two-dimensional hypothetical aquifer system for transient flow conditions. In the proposed ANN model, Cartesian coordinates
of observation wells, associated piezometric heads and observation time are used as inputs while corresponding transmissivity
and storativity values are used as outputs. The training, validation and testing processes of the ANN model are performed
under two scenarios. In scenario 1, all the sampled data are used through the simulation time. However, in the scenario 2,
there are data gaps due to irregular observations. By using the determined synaptic network weights, transmissivity and storativity
distributions are predicted. In addition, the performance of the proposed ANN is tested for different noise data conditions.
Results showed that the developed ANN model may be used in simultaneous aquifer parameter estimation problems. 相似文献
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Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different
methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be
of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be
appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary
attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding
to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models.
The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and
same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers,
namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the
synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily
compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and
dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated
using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates
the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input
data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity
analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas
of seismic data. 相似文献
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Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design. 相似文献
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遥感图像处理常见的困难有数据量巨大、噪声信息多，高度非线性及其导致的难以用解析或表述处理模型等。人工神经网络（artificial neural network,ANN)是由大量简单神经元广泛相互联接而成的非线性映射或自适应动力系统，可以解决上述问题，使用ANN进行遥感图像处理在遥感图像复原，变换和分类中有如下应用：（1）使用ANN和必要辅助数据从TM图像中提取地下火热辐射数据；（2）构造ANN非线性映射，利用TM1－5，7图像提高TM6图像空间分辨率；（3）模糊神经网络（FNN）遥感图像分类。 相似文献
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矿井煤层底板突水预测新方法研究 总被引：8，自引：1，他引：7
本文针对煤矿矿井煤层底板突水系统为一非线性系统的特性,提出采用对非线性问题具有良好适用性的人工神经网络系统（以下简称神经网络）,进行煤层底板突水预测。以作者们研制,使用神经网络的实践为基础,阐述系统、建模方法、适用条件和应用问题,并在焦作矿务局演马庄矿、焦作金科尔集团方庄煤矿对所建立的煤层底板突水预测神经网络进行生产性检验,取得良好的结果,说明该系统应用于煤层底板突水预测的可靠性。 相似文献
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人工神经网络模型在地下水水质评价分类中的应用 总被引：20，自引：0，他引：20
人工神经网络(ArtificialNeuralNetwork以下简称ANN)是一种行之有效的数据处理和分析方法,它的应用领域不断扩大并逐渐完善,本文在传统ANN方法基础上进行了进一步的探讨,立足于BP算法,通过调整ANN输出结构,提高其鲁棒性能,从而使其更具有适应性。将改进后的ANN应用于地下水水质评价分类,并和模糊综合评判评价结果进行了比较,分类结果令人满意。 相似文献
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This paper presents Artificial Neural Network (ANN) prediction models which relate permeability, maximum dry density (MDD)
and optimum moisture content with classification properties of the soils. The ANN prediction models were developed from the
results of classification, compaction and permeability tests, and statistical analyses. The test soils were prepared from
four soil components, namely, bentonite, limestone dust, sand and gravel. These four components were blended in different
proportions to form 55 different mixes. The standard Proctor compaction tests were adopted, and both the falling and constant
head test methods were used in the permeability tests. The permeability, MDD and optimum moisture content (OMC) data were
trained with the soil’s classification properties by using an available ANN software package. Three sets of ANN prediction
models are developed, one each for the MDD, OMC and permeability (PMC). A combined ANN model is also developed to predict
the values of MDD, OMC, and PMC. A comparison with the test data indicates that predictions within 95% confidence interval
can be obtained from the ANN models developed. Practical applications of these prediction models and the necessary precautions
for using these models are discussed in detail in this paper. 相似文献
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Marcellus Shale Lithofacies Prediction by Multiclass Neural Network Classification in the Appalachian Basin 总被引：4，自引：0，他引：4
Marcellus Shale is a rapidly emerging shale-gas play in the Appalachian basin. An important component for successful shale-gas reservoir characterization is to determine lithofacies that are amenable to hydraulic fracture stimulation and contain significant organic-matter and gas concentration. Instead of using petrographic information and sedimentary structures, Marcellus Shale lithofacies are defined based on mineral composition and organic-matter richness using core and advanced pulsed neutron spectroscopy (PNS) logs, and developed artificial neural network (ANN) models to predict shale lithofacies with conventional logs across the Appalachian basin. As a multiclass classification problem, we employed decomposition technology of one-versus-the-rest in a single ANN and pairwise comparison method in a modular approach. The single ANN classifier is more suitable when the available sample number in the training dataset is small, while the modular ANN classifier performs better for larger datasets. The effectiveness of six widely used learning algorithms in training ANN (four gradient-based methods and two intelligent algorithms) is compared with results indicating that scaled conjugate gradient algorithms performs best for both single ANN and modular ANN classifiers. In place of using principal component analysis and stepwise discriminant analysis to determine inputs, eight variables based on typical approaches to petrophysical analysis of the conventional logs in unconventional reservoirs are derived. In order to reduce misclassification between widely different lithofacies (for example organic siliceous shale and gray mudstone), the error efficiency matrix (ERRE) is introduced to ANN during training and classification stage. The predicted shale lithofacies provides an opportunity to build a three-dimensional shale lithofacies model in sedimentary basins using an abundance of conventional wireline logs. Combined with reservoir pressure, maturity and natural fracture system, the three-dimensional shale lithofacies model is helpful for designing strategies for horizontal drilling and hydraulic fracture stimulation. 相似文献